20179620181203023619.01099-430010.3390/e16095078doi000343110100020ISIARTICLEHierarchical sensor placement using joint entropy and the effect of modeling errorBasel2014MDPI AG201424Journal ArticlesGood prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.joint entropyhierarchical data structuressystem identificationsensor placementComputational Fluid Dynamics (CFD)Papadopoulou, MariaRaphael, Benny115051241201Smith, Ian F. C.106443241981Sekhar, Chandra5078-5101Entropy16n/a3103612n/ahttp://infoscience.epfl.ch/record/201796/files/entropy-16-05078-as-published.pdfIMAC252031U10237oai:infoscience.tind.io:201796ENACarticle188416188416188416EPFL-ARTICLE-201796EPFLPUBLISHEDREVIEWEDARTICLE